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Binary linear decision tree with genetic algorithm

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4 Author(s)
Bin-Bing Chai ; Dept. of Comput. Eng. & Comput. Sci., Missouri Univ., Columbia, MO, USA ; Xinhua Zhuang ; Yunxin Zhao ; J. Sklansky

A linear decision binary tree structure is proposed in constructing piecewise linear classifiers with the genetic algorithm (GA) being shaped and employed at each nonterminal node to search for a linear decision function optimal in the sense of maximum impurity reduction. The methodology works for both the two-class and multiclass cases. In comparison to several other well known methods, the proposed binary tree-genetic algorithm (BTGA) is demonstrated to produce a much lower cross validation misclassification rate. Finally, a modified BTGA is applied to the important pap smear cell classification. This results in a spectrum for the combination of the highest desirable sensitivity along with the lowest possible false alarm rate. The multiple choices offered by the spectrum for the sensitivity-false alarm rate combination will provide the flexibility needed for the pap smear slide classification

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:4 )

Date of Conference:

25-29 Aug 1996